Image('images/pydata_logo.png', width='300px')
IFrame('https://pydata.org/code-of-conduct/', **default_size)
IFrame('https://numfocus.org/sponsored-projects', **default_size)
Image('images/pydata-map.png', **default_size)
Image('images/pydata-uk-map.png', **default_size)
Image('images/pydata-london-screenshot.png', **default_size)
Come join the community on on Slack, Twitter or Linkedin
Come join the community on our UK-wide Slack channel!
Image('images/slack_join_qr.png', width='360px')
Image('images/twitter_qr_code.png', width='360px')
Image('images/linkedin_qr_code.png', width='360px')
Image('images/schedule_qr_code.png', width='360px')
Image('images/pydata_london_2023.png', **default_size)
Image('images/schedule_qr_code.png', width='360px')
In this talk, we demonstrate how to perform billion-scale facial recognition using the deepface package for Python and the approximate nearest neighbour algorithm. We have a database of billions of faces, and we show how to create embeddings for each face with deepface and use Faiss to search for an identity in milliseconds, even in such a large database.
We are trying to find a method that will allow us to automatically crack protocols of predictable differentiation from stem cells into any needed cell type (f.i. human neurons) We use microscopy, CV algorithms, optimizing algorithms, machine learning, and robotics for that purpose. I'm going to speak about the first stage of our project, where we created a cheap open-source microscope that we are using for data collection and manipulating samples and substances.